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---
license: apache-2.0
base_model:
- black-forest-labs/FLUX.1-dev
new_version: black-forest-labs/FLUX.1-dev
pipeline_tag: text-to-image
library_name: adapter-transformers
---
# TLCM: Training-efficient Latent Consistency Model for Image Generation with 2-8 Steps
<p align="center">
📃 <a href="https://arxiv.org/html/2406.05768v5" target="_blank">Paper</a> •
</p>
<!-- **TLCM: Training-efficient Latent Consistency Model for Image Generation with 2-8 Steps** -->
<!-- Our method accelerates LDMs via data-free multistep latent consistency distillation (MLCD), and data-free latent consistency distillation is proposed to efficiently guarantee the inter-segment consistency in MLCD.
Furthermore, we introduce bags of techniques, e.g., distribution matching, adversarial learning, and preference learning, to enhance TLCM’s performance at few-step inference without any real data.
TLCM demonstrates a high level of flexibility by enabling adjustment of sampling steps within the range of 2 to 8 while still producing competitive outputs compared
to full-step approaches. -->
we propose an innovative two-stage data-free consistency distillation (TDCD) approach to accelerate latent consistency model. The first stage improves consistency constraint by data-free sub-segment consistency distillation (DSCD). The second stage enforces the
global consistency across inter-segments through data-free consistency distillation (DCD). Besides, we explore various
techniques to promote TLCM’s performance in data-free manner, forming Training-efficient Latent Consistency
Model (TLCM) with 2-8 step inference.
TLCM demonstrates a high level of flexibility by enabling adjustment of sampling steps within the range of 2 to 8 while still producing competitive outputs compared
to full-step approaches.
## This is for Flux-base LoRA. |